Tuesday, 14 October 2014

Aims and Scope

Brain Computer Interfaces (BCI) aims at
establishing a one or two-way communication protocol between the human
brain and an electronic device. The research umbrella of BCI has
different names and overlaps with different research areas that evolved
under the wider objective of connecting human data to an electronic
device of some sort. Some of these areas include: adaptive automation,
augmented cognition, brain-machine interface, human-machine symbiosis,
and human-computer symbiosis.

The last decade has witnessed a
rise in the number of researchers working on BCI. With the advances of
sensor technologies, efficient signal processing algorithms, and
parallel computing, it was possible to finally realize the dream of many
researchers who talked about the concept in one form or another in the
sixties and seventies including J.C.R. Licklider, R.B. Rouse, and
others. Different sensor and measurement technologies are evolving
rapidly from the classical functional magnetic resonance imaging (fMRI),
functional near infrared (fNIR), Electroencephalography (EEG), to
complex integrated psycho-physiological sensor arrays.

Researchers
in Computational Intelligence have been better situated than ever to
extract knowledge from these signals, transform it to actionable
decisions, and designing the intelligent machine that has long been
promised and is now overdue. Success has been seen in many medical
applications including assisting people on wheelchairs, stroke
rehabilitation, and epileptic seizures. In the non-medical domain, BCI
has been used for computer games, authentication in cyber security, and
air traffic control.

This special issue aims at showcasing the
most exciting and recent advances in BCI and related topics. The guest
editors invite submissions of previously unpublished, recent and
exciting research on BCI. The special issue welcomes survey, position,
and research papers

Topics of Interest include:

Adaptive control schemes for BCI

Applications

Augmented cognition and adaptive aiding using BCI

Big data for brain mining

Collaborative multi-humans BCI environments

Computational intelligence applications for BCI

Data and signal processing techniques for BCI applications

Evolutionary algorithms for BCI

Fusion of heterogeneous psycho-physiological sensors

Fuzzy logic for BCI

Neuroplasticity induced by brain-computer interactions

Neural networks for BCI

Novel sensor technologies for BCI

Related computational intelligence methods for BCI

Situation awareness systems for BCI applications

Swarm techniques for BCI

Other closely related topics on computational intelligence for BCI

Submission Process

The
maximum length for the manuscript is typically 25 pages in single
column format with double-spacing, including figures and references.
Authors should specify on the first page of their manuscripts the
corresponding author’s contact and up to 5 keywords. Submission should
be made via https://www.easychair.org/conferences/?conf=ieeecimbci2016

Monday, 13 October 2014

Emulating brain-like learning performance has been a key challenge
for research in neural networks and learning systems, including
recognition, memory and perception. In the last few decades, a variety
of approaches for brain-like learning and information processing have
been proposed, including approaches based on sparse representations or
hierarchical/deep architectures. While capable of achieving impressive
performance, these methods still perform poorly compared to biological
systems under a wide variety of conditions. With the availability of
neuromorphic hardware providing a fundamentally different technique for
data representation, neuromorphic systems, using neural spikes to
represent the outputs of sensors and for communication between computing
blocks, and using spike timing based learning algorithms, have shown
appealing computing characteristics. However, current neuromorphic
learning systems cannot yet achieve the performance figures comparable
to what machine learning approaches can offer. Neuromorphic systems are
also compatible with another framework called cyborg intelligence.
Cyborg intelligence aims to deeply integrate machine intelligence with
biological intelligence by connecting machines and living beings via
brain-machine interfaces, enhancing strengths and compensating for
weaknesses by combining the biological cognition capability with the
machine computational capability. In cyborg intelligence, the real-time
interaction and exchange of information between biological and
artificial neural systems is still an important open challenge, and
existing learning approaches would not be able to meet such a challenge.
The goal of the special issue is to consolidate the efforts for
developing a suitable learning framework for neuromorphic systems and
cyborg intelligence and promote research activities in this area.

Scope of the Special Issue

We
invite original contributions related to learning in neuromorphic
systems and cyborg intelligence, from theories, algorithms, modelling
and experiment studies to applications. Topics include but are not
limited to:

Submission Instructions

Submit the manuscript by 15th Nov 2014 at the TNNLS webpage (http://mc.manuscriptcentral.com/tnnls)
and follow the submission procedure. Please, clearly indicate on the
first page of the manuscript and in the cover letter that the manuscript
has been submitted to the special issue on Learning in Neuromorphic
Systems and Cyborg Intelligence. Send also an email to the guest editors
with subject “TNNLS special issue submission” to notify about your
submission.

Friday, 10 October 2014

Aims and Scope

Over the past decade or so, computational
intelligence techniques have been highly successful for solving big data
challenges in changing environments. In particular, there has been
growing interest in so called biologically inspired learning (BIL),
which refers to a wide range of learning techniques, motivated by
biology, that try to mimic specific biological functions or behaviors.
Examples include the hierarchy of the brain neocortex and neural
circuits, which have resulted in biologically-inspired features for
encoding, deep neural networks for classification, and spiking neural
networks for general modelling.

To ensure that these models are
generalizable to unseen data, it is common to assume that the training
and test data are independently sampled from an identical distribution,
known as the sample i.i.d. assumption. In dynamic and non-stationary
environments, the distribution of data changes over time, resulting in
the phenomenon of ‘concept drift’ (also known as population drift or
concept shift), which is a generalization of covariance shift in
statistics. Over the last five years, transfer learning and multitask
learning have been used to tackle this problem. Fundamental analyses
using probably approximately correct (PAC) and Rademacher complexity
frameworks have explained why appropriate incorporation of context and
concept drift can improve generalizability in changing environments.

It
is possible to use human-level processing power to tackle concept drift
in changing enviroments. Concept drift is a real-world problem, usually
associated with online and concept learning, where the relationships
between input data and target variables dynamically change over time.
Traditional learning schemes do not adequately address this issue,
either because they are offline or because they avoid dynamic learning.
However, BIL seems to possess properties that would be helpful for
solving concept drift problems in changing environments. Intuitively,
the human capacity to deal with concept drift is innate to cognitive
processes, and the learning problems susceptible to concept drift seem
to share some of the dynamic demands placed on plastic neural areas in
the brain. Using improved biological models in neural networks can
provide insight into cognitive computational phenomena.

However, a
main outstanding issue in using computational intelligence for changing
enviroments and domain adaptation is how to build complex networks, or
how networks should be connected to the features, samples, and
distribution drifts. Manual design and building of these networks are
beyond current human capabilities. Recently, computational intelligence
methods has been used to address concept drift in changing enviroments,
with promising results. A Hebbian learning model has been used to handle
random, as well as correlated, concept drift. Neural networks have been
used for concept drift detection, and the influence of latent variables
on concept drift in a neural network has been studied. In another
study, a timing-dependent synapse model has been applied to concept
drift. These works mainly apply biologically-plausible computational
models to concept drift problems. Although these results are still in
their infancy, they open up new possibilities to achieve brain-like
intelligence for solving concept drift problems in changing
environments.

Taking the current state of research in
computational intelligence for changing environments into account, the
objective of this special issue is to collate this research to help
unify the concepts and terminology of computational intelligence in
changing environments, and to survey state-of-the-art computational
intelligence methodologies and the key techniques investigated to date.
Therefore, this special issue invites submissions on the most recent
developments in computational intelligence for changing enviroments
algorithms and architectures, theoretical foundations, and
representations, and their application to real-world problems. We also
welcome timely surveys and review papers.

Submission Process

The
maximum length for the manuscript is typically 25 pages in single
column format with double-spacing, including figures and references.
Authors should specify in the first page of their manuscripts the
corresponding author’s contact and up to 5 keywords. Submission should
be made via